CN106842368B - A kind of single-point precipitation forecast method based on Beidou positioning - Google Patents
A kind of single-point precipitation forecast method based on Beidou positioning Download PDFInfo
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- G—PHYSICS
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- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
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Abstract
The invention belongs to meteorology technical fields, specifically provide a kind of single-point precipitation forecast method based on Beidou positioning.The present invention is by being improved to the scoring of multi-grade percentage to precipitation TS scoring, it obtains and is based on DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM lattice point precipitation data, then data anastomosing algorithm is used, by multi-source precipitation measurement data fusion, obtain each lattice point precipitation forecast data, it is accurately positioned in conjunction with Beidou, and the single-point precipitation forecast of building quantifies accounting equation, determines single-point precipitation forecast value.Using single-point precipitation forecast method of the present invention, forecast accuracy is improved, is further rescue and relief work, the Meteorological Services of key project etc. provide technical support.
Description
Technical field
The present invention relates to meteorology technical fields, and in particular to a kind of single-point precipitation forecast method based on Beidou positioning.
Background technique
Single-point precipitation forecast refers in certain place of no meteorological observation website, carries out the action such as rescue and relief work, construction
The precipitation fine forecast of Shi Suoxu.The fining degree and accuracy rate of forecast have weight for commanding and decision-making and organization arrangement
It influences.
It is routinely to be found using rough location information apart from the place in previous emergency service single-point precipitation forecast
Certain nearest numerical forecast Grid data as basic document, be aided near the meteorological datas such as automatic Weather Station, radar, sounding into
Row artificial correction.Due to the distribution of meteorological observation website and uneven, density, frequency, the precision of primary data are different, no
Accuracy rate of the same numerical model in different regions is all variant, is come with a kind of nearest lattice point predicted value of the distance of numerical model
As master data, it is understood that there may be apparent error；The considerations of this method is lacked to weather system and weather situation simultaneously, this
Amplification for precipitation error is significantly, especially under the complicated landforms such as mountain area, forest, high-speed rail route, culvert；Manually
There is also instability problems for amendment.
Since the 1970s, the main direction of development of the numerical weather forecast as global prediction technology, to meteorological section
Development brings revolutionary progress, supports the constantly improve of forecast accuracy.Since atmosphere is nonlinearity system
It unites, physical process has uncertainty in atmosphere initial value and numerical model, along with the fast development of computer technology, set
It forecasts that this random forecast method represents the developing direction of numerical forecast in recent years, promotes Numerical Forecast Technology and weather
The progress of forecasts services.But in DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM, for discontinuous variable as precipitation, the value of forecasting of evaluation profile is used
TS scoring, TS=100, otherwise TS=0 when routine assessments are fact and forecast is precipitation at the same level.This assessment mode is directly led
It has caused when a certain model predictions precipitation rank has error, which cannot play a role in DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM.So how
It is timely and accurately accurately positioned and provided accurate single-point precipitation forecast value to emergency service point, becomes emergency Meteorological Services
One crucial problem.
Summary of the invention
The object of the present invention is to provide a kind of a variety of Numerical Prediction Models DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM results of comprehensive utilization and multi-source precipitation
Data is accurately positioned, the method for Exact Forecast single-point precipitation in conjunction with Beidou.Specific single-point precipitation forecast F value determination is as follows:
Wherein, FkjiFor the precipitation forecast value of 4 lattice points of j-th of lattice point of kth layer, i=1,2,3,4, k=1,2 ... ...,
N, j=1,2 ... ..., m；The lattice point number that m is each layer；RkjiFor the distance between 4 lattice points and F point；FkjIt is j-th of kth layer
The precipitation forecast value of lattice point；RkjFor the distance between j-th of lattice point of kth layer and F point, FkFor the precipitation forecast value of kth layer lattice point；
a0, a1..., anFor the regression coefficient of the multiple linear regression equations of single-point precipitation；N is determined according to the confidence level set as standard.
Further, the precipitation forecast value determination of each lattice point is as follows:
The Numerical Rainfall Forecast Grid data method based on DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM of acquisition is as follows:
Wherein, FkjiFor the precipitation forecast value of 4 lattice points of j-th of lattice point of kth layer, i=1,2,3,4, k=1,2 ... ...,
N, j=1,2 ... ..., m；The lattice point number that m is each layer, FkjisFor the lattice point precipitation forecast value of s kind Numerical Prediction Models, s=
1,2 ... ..., p, p are the kind number of DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM；TSsFor the precipitation forecast percentage score value of s kind Numerical Prediction Models；
Numerical Rainfall Forecast Grid data based on DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM merges multi-source precipitation measurement data using meteorological data
Method obtains the precipitation forecast value of each lattice point.
Preferably, the precipitation forecast percentage score value preparation method are as follows: percentage, which scores 0-100 points, in advance is
Precipitation event is divided into multiple magnitudes by multiple magnitudes, obtains the corresponding drop by experience or the statistical value of experiment
Water forecasts percentage score value.
Further, the scoring magnitude is divided according to required precision.
Preferably, the scoring magnitude is divided into 0,30,50,90,100,5 magnitudes；Precipitation event is divided into
10 nothing, light rain, drizzle or moderate rain, moderate rain, moderate rain or heavy rain, heavy rain, heavy or torrential rain, heavy rain, torrential rain, extra torrential rain magnitudes.
Preferably, the meteorological data fusion method uses Three-dimensional Variational Data Assimilation data fusion method.
Preferably, the distance between each lattice point and the F point are positioned according to Beidou and are determined.
Preferably, confidence alpha=0.05.
The present invention is utilized by integrated use majority value set of modes forecasting mode and multi-source minute grade precipitation data
Dipper system is accurately positioned the place, constructs calculating side with distance weighted linear interpolation method and multiple linear regression analysis method
Journey has reached based on most value set of modes forecast systems, the various observed patterns of fusion utilization, height, frequency, density,
The fining precipitation data that precision is all different, be based on Beidou precision positioning, any single-point precipitation fine forecast is provided as a result,
Improve forecast accuracy.Technical support is provided for Weather-services such as China's rescue and relief work, key projects, is had important
Application value.
Detailed description of the invention
Fig. 1 is that the present invention is based on the single-point precipitation forecast method flow charts of Beidou positioning.
Fig. 2 is the middle emergency service point obtained according to embodiments of the present invention n-layer grid schematic diagram nearby.
It forecasts to illustrate with live comparison for 24 hours by 3 hours precipitation when Fig. 3 is day 08 4-9 of in August, 2016 in embodiment
Figure.
Illustrate by precipitation 48h forecast in 3 hours with live comparison when Fig. 4 is day 08 4-9 of in August, 2016 in embodiment
Figure.
Specific embodiment
With reference to the accompanying drawing, using the emergency Meteorological Services of a gas pipeline accident as example, the present invention is done further
Description.The background of this emergency service is that on July 20th, 2016, precipitation causes mud-rock flow to come down, and causes middle petrochemical industry river gas eastern
Pipeline burst near the enshi Jianshi County town the Cui Ba valve chamber sent, needs to provide leakage point fine precipitation for the headquarter that speedily carry out rescue work
Forecast.It is necessarily pointed out that following specific embodiments are served only for that the present invention is further detailed, Bu Nengli
Solution is limiting the scope of the invention, and person skilled in art can make one to the present invention according to foregoing invention content
A little nonessential modifications and adaptations.
A kind of single-point precipitation forecast method based on Beidou positioning proposed according to the present invention, it is pre- by building single-point precipitation
Quantitative accounting equation is reported, the precision that any single-point precipitation forecast calculates is ensure that, improves computational efficiency, for rescue and relief work, again
The Weather-services such as point engineering provide technical support.
A kind of single-point precipitation forecast method based on Beidou positioning proposed by the present invention, as shown in Figure 1, specific as follows:
Step 1: obtaining the Numerical Rainfall Forecast Grid data based on DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM
It can solve the uncertain problem of each mode to a certain extent using DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM mode, but pre- in conventional combination
In report, for discontinuous variable as precipitation, the value of forecasting of evaluation profile is scored using percentage TS, and routine assessments are real
TS=100, otherwise TS=0 when condition and forecast are precipitation at the same level.This assessment mode is directly resulted in when a certain model predictions
When precipitation rank has error, which cannot play a role in DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM.Therefore, the present invention is commented using the TS of percentage
TS scoring 0-100 is divided for multiple magnitudes, how many a grades is specifically divided to divide according to required precision by point method, while by rain
Magnitude is also divided.It forecasts to classify with live precipitation, such percentage TS scoring can more accurately consider each
Influence of the mode to DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM.
In the present invention, being set with p kind, (s=1,2 ... p) Numerical Prediction Models obtain the precipitation number based on DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM
Value forecast Grid data method is as follows:
Wherein, Fkji(i=1,2,3,4) is the 4 of kth layer (k=1,2 ... ..., n) j-th of lattice point (j=1,2 ... ..., m)
The precipitation forecast value of a lattice point, the lattice point number that m is each layer, FkjisFor s kind (s=1,2 ... ..., p) Numerical Prediction Models
Lattice point precipitation forecast value, TSsFor the precipitation forecast TS percentage score value of s kind Numerical Prediction Models.Precipitation forecast TS percentage
Number score value preparation methods are as follows: percentage scoring 0-100 is divided for multiple magnitudes in advance, precipitation event is divided into multiple amounts
Grade obtains corresponding precipitation forecast percentage score value by experience or the statistics of experiment, as shown in table 1:
1 model predictions of table TS percentage grade form corresponding with precipitation fact
Table 1 is model predictions and the live corresponding TS percentage grade form of precipitation, R in tablefIt is (single for model predictions precipitation
Position: mm), RobsFor live precipitation (unit: mm)；The percentage of corresponding different Precipitation, TS scoring is divided into 0,30,50,
90,100 totally 5 magnitudes.
There are many kinds of above-mentioned Numerical Prediction Models, such as: common several Numerical Prediction Models international and domestic at present:
GRAPES、T639、ECMWF、NCEP……
GRAPES:GRAPES is the abbreviation (Global/Regional in full name in English " whole world/region assimilation forecast system "
Assimilation and PrEdiction System), it is the Numerical Prediction System of new generation of China Meteorological Administration's independent research,
The formal business in 2015.
T639:T639 is the global numerical operational forecast system developed by China Meteorological Administration's numerical forecast center, is
The abbreviation of the whole world T639L60 medium-range numerical forecast mode.Mode resolution ratio with higher reaches global horizontal resolution 30
Kilometer, 60 layers of vertical resolution, top of model reaches 0.1 hundred pas.In formal service operation in 2008.
ECMWF:ECMWF is the abbreviation of " European medium-range numerical forecast ", and Numerical Prediction Models are that global development is earliest, smart
Spend higher, stable business model, in the service operation of decades, the forecast level of ECMWF numerous numerical value in the world
The position of superior top-ranking level is in Forecast Mode.
NCEP:NCEP is Environmental forecasting centre (National Center of Environment
Prediction abbreviation), multiple business Numerical Prediction Models are in first-class level, such as meso-scale model in the world
MM5 and WRF.
Step 2: obtaining distance and the n-layer precipitation centered on the point between the point and each lattice point of Beidou position matching
Grid data
Beidou positioning accuracy is 20 meters in Asia-Pacific range in 2016, is equipped with area (such as Beijing) precision at difference station in part
Reach meter level.In the place of Beidou covering, mobile phone, the Vehicular satellite navigation device that chip is received equipped with Beidou satellite navigation are used
Or other Beidou terminals, that is, it is able to achieve the positioning of somewhere point.The point is obtained at a distance from each lattice point by Beidou position matching,
And the Numerical Rainfall Forecast Grid data based on DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM obtained in conjunction with step 1 obtains the n centered on the point
Layer precipitation Grid data.
Fig. 2 is n-layer grid schematic diagram near the emergency service point obtained using the method for the present invention, it can be seen from the figure that
By the positioning of matching Beidou and precipitation Grid data, it is attached the emergency service point enshi Jianshi County town Cui Ba paddy field dam village has been obtained
The precipitation Grid data of nearly n-layer 5x5 km, can finely forecast to provide basic data for the precipitation of leakage point.From Figure 2 it can be seen that
For the n-layer grid near emergency service point, kth layer (k=1,2,3 ..., n) have respectively j lattice point (j=1,8,16 ...,
m)。
Step 3: multi-source precipitation measurement data are used data fusion based on the n-layer precipitation Grid data that step 2 obtains
Method obtains new n-layer precipitation Grid data.
Based on the n-layer precipitation Grid data that step 2 obtains, multi-source meteorological measuring is obtained using data fusion method
Take new n-layer precipitation Grid data.Wherein, multi-source meteorological measuring from ground observation, radar, satellite, wind profile instrument,
Microwave radiometer, aviation report, aircraft report, GPS steam, sounding etc..Data fusion method is mathematical algorithm, and there are many kinds of meteorological numbers
According to fusion method, the present invention uses Three-dimensional Variational Data Assimilation data fusion method, and certain other fusion methods are not also repelled, but according to
Current condition uses Three-dimensional Variational Data Assimilation data fusion method for optimum efficiency by experiment.For example single-point is obtained nearby by 3
Hour n-layer 5x5 km Numerical Rainfall Forecast Grid data.
Step 4: determining single-point precipitation forecast value
Using distance weighted linear interpolation method and linear regression method, the quantitative calculating to single-point precipitation is realized.Tool
The group of equations of body is as follows:
Wherein, Fkji(i=1,2,3,4) is the 4 of kth layer (k=1,2 ... ..., n) j-th of lattice point (j=1,2 ... ..., m)
The precipitation forecast value of a lattice point, the lattice point number that m is each layer, RkjiFor the distance between 4 lattice points and F website, FkjFor kth layer
The precipitation forecast value of j-th of lattice point；RkjFor the distance between j-th of lattice point of kth layer and website, FkFor the precipitation of kth layer lattice point
Predicted value；F is website precipitation forecast value, a0, a1..., anFor the regression coefficient of the multiple linear regression equations of single-point precipitation, return
Return coefficient application least square method to solve, exactly solves regression coefficient under the conditions of keeping residual error quadratic sum the smallest.N root
It is determined according to the confidence level set as standard, such as confidence alpha=0.05.
Above-mentioned equation group is precipitation computation model, for emergency service point, for the numerical forecast grid of n-layer 5x5 km,
Using linear interpolation distance weighted twice and a multiple linear regression, equation group is solved, can quantitatively calculate somewhere point
Precipitation forecast.Specific calculating process is as follows:
S401, the distance weighted interpolation of each lattice point
For the lattice point forecast data of 5x5 km, 4 distance weighted interpolation of numerical value of each lattice point are first obtained into the lattice
The value of point, accounting equation such as (2-1).
Wherein, FkjFor the precipitation forecast value of kth layer (k=1,2 ... ..., n) j-th of lattice point (j=1,2 ... ..., m), Fkji
(i=1,2,3,4) is the precipitation forecast value of 4 lattice points of j-th of lattice point of kth layer, RkjiFor 4 lattice points and emergency service website
The distance between.
S402, each distance weighted interpolation of layer lattice point
Distance weighted interpolation obtains each layer of precipitation value, and specific accounting equation is as follows:
Wherein, FkFor the precipitation forecast value of kth layer lattice point, FkjFor the precipitation forecast value of j-th of lattice point of kth layer；RkjFor kth
Layer the distance between j-th of lattice point and website.
S403, website precipitation multiple linear regression equations
Precipitation multiple linear regression equations are constructed, the precipitation calculated result of website is obtained.
The principle of building regression equation is that, due to using interpolation and matching treatment to data, the precipitation of each layer calculates
There are errors with measured value for value, analyze through deviation statistics, establish regression equation and calculated.When establishing regression equation, to set
Believe that horizontal α=0.05 is standard, analyzes the relationship of precipitation calculation amount and measured value, precipitation is calculated by building regression equation.
Specific regression equation is as follows:
Wherein, F is precipitation forecast value, FkFor the precipitation forecast value of kth layer lattice point, a0, a1..., anFor multiple linear regression
The regression coefficient of equation.Regression coefficient application least square method solves, and exactly makes the smallest condition of residual error quadratic sum
Lower solution regression coefficient.
The precipitation forecast model constructed by above-mentioned equation group can acquire single-point precipitation forecast result.
By utilizing method of the invention, most value set of modes forecast and multi-source precipitation fining observation number are comprehensively utilized
According to the precipitation progress Exact Forecast of emergency service point (near the town Cui Ba of enshi).Emergency service point is in hilly and mountainous land,
Topography and geomorphology is more complicated, has a certain distance apart from existing weather station, if directly using the data of neighbouring website,
Biggish error is had, rescue and relief work effect is directly affected.Using method of the invention, in the emergency service for continuing 1 month,
Exact Forecast is carried out for the precipitation of emergency service point, precipitation forecast accuracy rate is significantly improved relative to conventional method, takes
Obtained good service effectiveness.Precipitation Process service in August 4-8 days is exactly representative instance.
Table 2 is to apply Jianshi station provided by the invention precipitation forecast and live inspection result, and the date has referred to report day in table
Phase, when to act section of giving the correct time be 20, TS is that TS scores, and weather refers to weather forecast accuracy.As can be seen from Table 2, being forecast for 24 hours with 48h weather
Accuracy rate is 100%, i.e., this method provide precipitation whether there is or not accuracy rate it is very high, for 24 hours with the precipitation forecast TS of 48h scoring exist
50 points or more, extra torrential rain day is removed, the TS scoring of precipitation reaches 70 points or more；Prove that the inventive method has service application valence
Value.
The forecast of 2 Jianshi station daily precipitation of table and live inspection result statistical form
Fig. 3, Fig. 4 are the comparison diagram of accurate precipitation forecast and fact that reference station is obtained using the method for the present invention, reference station
What is selected is the automatic Weather Station nearest apart from emergency service point, the town the Ji Cuiba paddy field Jianshi Ba Cun station, wherein Fig. 3 and Fig. 4 are respectively
When day 08 4-9 of in August, 2016 forecast for 24 hours by 3 hours precipitation and 48h forecast and live comparison diagram.As seen from the figure, this is utilized
Invention provides consistent with live trend by 3 hours precipitation forecasts, has forecast 5 Precipitation Process；Especially to medium and small precipitation
Time, magnitude forecast precision it is higher；Apparent error appears in Severe Storm Rainfall Event, and it is significantly inclined to be reflected as Precipitation forecast amount
Small, there is period lag in Precipitation forecast for 24 hours.To the magnitude and time of occurrence by 3 hours precipitation, it is pre- to be better than 48h for forecast for 24 hours
Report, it was demonstrated that the validity of minute grade precipitation fact fusion application.
Be worth it is important to note that, although it is significant to the Precipitation Forecast error of Severe Storm Rainfall Event on the 5th, forecast it is current
Section lag, but small probability event this for extra torrential rain, it is provided by the invention by 3h Forecast of Precipitation in high resolution relatively accurately
This great rainfall process is featured, the accumulation precipitation of forecast reaches heavy rain magnitude, and the period for obvious precipitation occur also compares
It is relatively accurate, therefore forecast model products promptly and accurately are provided for emergency service point, the effect of emergency service is preferable.
The purpose of the present invention, technical solution and embodiment is described in detail in above-described specific descriptions, institute
It should be understood that the specific example of invention described above, is not intended to limit the scope of protection of the present invention, it is all in the present invention
Spirit within principle, any modification, equivalent substitution, improvement and etc. done, and with new money after the relevant technologies progress
Material source, Forecast Mode and the increase or improvement of forecasting the refinement of range spatial and temporal scales, statistical product, interpolation and homing method,
It should be included within protection scope of the present invention.
Claims (8)
1. a kind of single-point precipitation forecast method based on Beidou positioning, it is characterised in that: the single-point precipitation forecast F value determines such as
Under:
Wherein, FkjiFor the precipitation forecast value of 4 lattice points of j-th of lattice point of kth layer, i=1,2,3,4, k=1,2 ... ..., n, j
=1,2 ..., m；The lattice point number that m is each layer；RkjiFor the distance between 4 lattice points and F point；FkjFor j-th of lattice point of kth layer
Precipitation forecast value；RkjFor the distance between j-th of lattice point of kth layer and F point, FkFor the precipitation forecast value of kth layer lattice point；a0,
a1,…,anFor the regression coefficient of the multiple linear regression equations of single-point precipitation；N is determined according to the confidence level set as standard.
2. the single-point precipitation forecast method according to claim 1 based on Beidou positioning, it is characterised in that: each lattice point
Precipitation forecast value determination it is as follows:
The Numerical Rainfall Forecast Grid data method based on DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM of acquisition is as follows:
Wherein, FkjiFor the precipitation forecast value of 4 lattice points of j-th of lattice point of kth layer, i=1,2,3,4, k=1,2 ... ..., n, j
=1,2 ..., m；The lattice point number that m is each layer, FkjisFor the lattice point precipitation forecast value of s kind Numerical Prediction Models, s=1,
2 ... ..., p, p are the kind number of DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM；TSsFor the precipitation forecast percentage score value of s kind Numerical Prediction Models；
Multi-source precipitation measurement data are used meteorological data fusion method by the Numerical Rainfall Forecast Grid data based on DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM
Obtain the precipitation forecast value of each lattice point.
3. the single-point precipitation forecast method according to claim 2 based on Beidou positioning, it is characterised in that: the precipitation is pre-
Report percentage score value preparation method are as follows: percentage is scored 0-100 points as multiple magnitudes in advance, precipitation event is divided into
Multiple magnitudes obtain the corresponding precipitation forecast percentage score value by experience or the statistical value of experiment.
4. the single-point precipitation forecast method according to claim 3 based on Beidou positioning, it is characterised in that: the scoring amount
Grade is divided according to required precision.
5. the single-point precipitation forecast method according to claim 3 based on Beidou positioning, it is characterised in that: by the scoring
Magnitude is divided into 0,30,50,90,100,5 magnitudes；By precipitation event be divided into nothing, light rain, drizzle or moderate rain, moderate rain, in big
10 rain, heavy rain, heavy or torrential rain, heavy rain, torrential rain, extra torrential rain magnitudes.
6. the single-point precipitation forecast method according to claim 2 based on Beidou positioning, it is characterised in that: the meteorology number
Three-dimensional Variational Data Assimilation data fusion method is used according to fusion method.
7. the single-point precipitation forecast method according to claim 1 based on Beidou positioning, it is characterised in that: each lattice point and institute
It states the distance between F point and determination is positioned according to Beidou.
8. the single-point precipitation forecast method according to claim 1 based on Beidou positioning, it is characterised in that: the confidence level
α=0.05.
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| CN109615236A (en) * | 2018-12-13 | 2019-04-12 | 深圳市气象局 | Precipitation forecast mode checking methods of marking, system, terminal and storage medium |
| CN110703357B (en) * | 2019-04-30 | 2021-04-20 | 国家气象中心(中央气象台) | Global medium term numerical prediction GRAPES _ GFS method |
| CN110618473A (en) * | 2019-10-09 | 2019-12-27 | 北京北科融智云计算科技有限公司 | Instant meteorological information guarantee method |
| CN111538935B (en) * | 2019-12-26 | 2023-08-25 | 北京玖天气象科技有限公司 | Fine precipitation fusion method, system, electronic equipment and storage medium based on terrain features and multi-source mode products |
| CN113075751A (en) * | 2021-03-26 | 2021-07-06 | 国网河南省电力公司电力科学研究院 | Method and system for fusing observation data in short-term forecasting |
| CN113537648B (en) * | 2021-09-16 | 2021-12-24 | 国能日新科技股份有限公司 | Wind speed prediction method and device based on set data |
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